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相关实验视频

Updated: May 13, 2025

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
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使用改进的爬行动物搜索算法优化图像细分,使用Gbest运算符来实现多级值.

Laith Abualigah1, Nada Khalil Al-Okbi2, Saleh Ali Alomari3

  • 1Computer Science Department, Al Al-Bayt University, Mafraq, 25113, Jordan. aligah.2020@gmail.com.

Scientific reports
|April 13, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种增强的爬行动物搜索算法 (RSA),用于多层次的图像值. 改进的算法有效地优化了复杂的灰度和彩色图像的值,优于现有的方法.

关键词:
图像细分 图像细分 图像细分医学图像 医学图像 医学图像多层次的门值.奥苏方法,卡普尔方法.爬行动物搜索算法 爬行动物搜索算法

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科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 优化算法 优化算法

背景情况:

  • 双级值与包含多个对象或颜色的复杂图像作斗争.
  • 多级值对于复杂的图像细分至关重要,但会带来优化挑战.

研究的目的:

  • 提供一个改进的爬行动物搜索算法 (RSA) 与Gbest运算符,以提高多层次图像值性能.
  • 用基于的客观函数来确定灰度和彩色图像的最佳值.

主要方法:

  • 拟议的方法使用了增强的爬行动物搜索算法 (RSA),其中包含了Gbest操作符.
  • 来自Otsu和Kapur技术的基于的目标函数被用于优化.
  • 在16个基准图像上进行了实验,包括医疗 (COVID-19) 和标准颜色/灰度数据集.

主要成果:

  • 增强的RSA在多级值任务中表现出卓越的性能.
  • 使用健身函数,峰值信号与噪声比率 (PSNR),结构相似度指数 (SSIM) 和弗里德曼排名测试的定量评估证实了有效性.
  • 该算法在各种图像类型中被证明是稳健的.

结论:

  • 提议的增强爬行动物搜索算法为多层次图像值提供了强大而有效的解决方案.
  • 与现有的最先进的技术相比,这种方法显著提高了复杂图像的细分精度.